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未验证 提交 64adfe7a 编写于 作者: Z Zhang Jun 提交者： GitHub
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[inference][trt]trt support 0 dims (#53383) 

* trt support 0 dim

* trt support 0 dim

* update activation ut
上级 6d9bbee3
隐藏空白更改
内联 并排
Showing with 34 addition and 602 deletion
paddle/fluid/inference/tensorrt/convert/op_converter.h
浏览文件 @ 64adfe7a
......@@ -306,13 +306,15 @@ class OpConverter {
auto max_input_shape = engine->max_input_shape()[input];
auto optim_input_shape = engine->optim_input_shape()[input];
size_t ranks = min_input_shape.size();
if (ranks == 0) {
all_dynamic_shape_set = false;
LOG(INFO) << "trt input [" << input.c_str()
<< "] dynamic shape info not set, please check and retry.";
// check other input
continue;
}
// allow 0 dim for dynamic shape input
// if (ranks == 0) {
// all_dynamic_shape_set = false;
// LOG(INFO) << "trt input [" << input.c_str()
// << "] dynamic shape info not set, please check and
// retry.";
// // check other input
// continue;
// }
std::vector<int64_t> input_shape;
// input_shape.push_back(-1);
for (size_t i = 0; i < ranks; i++) {
......
paddle/fluid/inference/tensorrt/op_teller.cc
浏览文件 @ 64adfe7a
......@@ -116,9 +116,10 @@ struct SimpleOpTypeSetTeller : public Teller {
auto x_var_name = desc.Input("X")[0];
auto* x_var_desc = block->FindVar(x_var_name);
const auto x_shape = x_var_desc->GetShape();
if (x_shape.size() == 1) {
if (!with_dynamic_shape && (x_shape.size() == 1 || x_shape.size() == 0)) {
VLOG(3) << op_type
<< " op does not support input's dim is 1 in tensorrt.";
<< " op does not support input's dim is 1 or 0 in tensorrt "
"static shape mode.";
return false;
}
#if !IS_TRT_VERSION_GE(7000)
......
paddle/fluid/operators/tensorrt/tensorrt_engine_op.h
浏览文件 @ 64adfe7a
......@@ -39,6 +39,7 @@
#include "paddle/fluid/inference/tensorrt/engine.h"
#include "paddle/fluid/inference/tensorrt/helper.h"
#include "paddle/fluid/inference/utils/io_utils.h"
#include "paddle/utils/string/string_helper.h"
namespace paddle {
namespace inference {
......@@ -64,19 +65,10 @@ using inference::tensorrt::TRTInt8Calibrator;
static void RuntimeStaticShapeCheck(std::vector<int64_t> runtime_input_shape,
std::vector<int64_t> model_input_shape) {
auto comma_fold = [](std::string a, int b) {
return std::move(a) + ", " + std::to_string(b);
};
std::string model_input_shape_str =
std::accumulate(std::next(model_input_shape.begin()),
model_input_shape.end(),
std::to_string(model_input_shape[0]),
comma_fold);
string::join_strings(model_input_shape, ',');
std::string runtime_input_shape_str =
std::accumulate(std::next(runtime_input_shape.begin()),
runtime_input_shape.end(),
std::to_string(runtime_input_shape[0]),
comma_fold);
string::join_strings(runtime_input_shape, ',');
PADDLE_ENFORCE_EQ(
model_input_shape == runtime_input_shape,
true,
......@@ -137,24 +129,10 @@ static void RuntimeDynamicShapeCheck(
}
return true;
};
auto comma_fold = [](std::string a, int b) {
return std::move(a) + ", " + std::to_string(b);
};
std::string runtime_input_shape_str =
std::accumulate(std::next(runtime_input_shape.begin()),
runtime_input_shape.end(),
std::to_string(runtime_input_shape[0]),
comma_fold);
std::string min_input_shape_str =
std::accumulate(std::next(min_input_shape.begin()),
min_input_shape.end(),
std::to_string(min_input_shape[0]),
comma_fold);
std::string max_input_shape_str =
std::accumulate(std::next(max_input_shape.begin()),
max_input_shape.end(),
std::to_string(max_input_shape[0]),
comma_fold);
string::join_strings(runtime_input_shape, ',');
std::string min_input_shape_str = string::join_strings(min_input_shape, ',');
std::string max_input_shape_str = string::join_strings(max_input_shape, ',');
PADDLE_ENFORCE_EQ(is_input_shape_valid(
runtime_input_shape, min_input_shape, max_input_shape),
true,
......@@ -551,7 +529,6 @@ class TensorRTEngineOp : public framework::OperatorBase {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
auto stream = reinterpret_cast<const phi::GPUContext &>(dev_ctx).stream();
std::vector<std::string> output_maps =
Attr<std::vector<std::string>>("output_name_mapping");
......@@ -566,7 +543,6 @@ class TensorRTEngineOp : public framework::OperatorBase {
trt_context = engine->context();
binding_offset = engine->GetBindingsOffset();
}
// Bind input tensor to TRT.
for (const auto &x : runtime_input_names_) {
#if IS_TRT_VERSION_LT(8000)
......
test/ir/inference/test_trt_convert_activation.py
浏览文件 @ 64adfe7a
......@@ -33,7 +33,9 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest):
def sample_program_configs(self):
def generate_input1(dims, batch, attrs: List[Dict[str, Any]]):
if dims == 1:
if dims == 0:
return np.random.random([]).astype(np.float32)
elif dims == 1:
return np.random.random([32]).astype(np.float32)
elif dims == 2:
return np.random.random([3, 32]).astype(np.float32)
......@@ -42,7 +44,7 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest):
else:
return np.random.random([batch, 3, 32, 32]).astype(np.float32)
for dims in [1, 2, 3, 4]:
for dims in [0, 1, 2, 3, 4]:
for batch in [1, 4]:
for op_type in [
"relu",
......@@ -51,9 +53,13 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest):
"relu6",
"elu",
"selu",
"silu",
"softsign",
"stanh",
"thresholded_relu",
"celu",
"logsigmoid",
"tanh_shrink",
"softplus",
]:
# few samples to reduce time
......@@ -63,6 +69,8 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest):
for alpha in [0.67]:
self.dims = dims
dics = [{}]
if op_type == "celu":
dics = [{"alpha": 1.0}]
if op_type == "elu":
dics = [{"alpha": alpha}]
if op_type == "selu":
......@@ -103,7 +111,11 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest):
self, program_config
) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
if self.dims == 1:
if self.dims == 0:
self.dynamic_shape.min_input_shape = {"input_data": []}
self.dynamic_shape.max_input_shape = {"input_data": []}
self.dynamic_shape.opt_input_shape = {"input_data": []}
elif self.dims == 1:
self.dynamic_shape.min_input_shape = {"input_data": [1]}
self.dynamic_shape.max_input_shape = {"input_data": [64]}
self.dynamic_shape.opt_input_shape = {"input_data": [32]}
......@@ -132,7 +144,7 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest):
self.dynamic_shape.opt_input_shape = {}
def generate_trt_nodes_num(attrs, dynamic_shape):
if self.dims == 1:
if not dynamic_shape and (self.dims == 1 or self.dims == 0):
return 0, 3
return 1, 2
......
test/ir/inference/test_trt_convert_celu.py 已删除 100644 → 0
浏览文件 @ 6d9bbee3
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from functools import partial
from typing import Any, Dict, List
import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import TrtLayerAutoScanTest
import paddle.inference as paddle_infer
class TrtConvertCeluTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def sample_program_configs(self):
def generate_input1(dims, attrs: List[Dict[str, Any]]):
if dims == 1:
return np.ones([3]).astype(np.float32)
elif dims == 2:
return np.ones([3, 64]).astype(np.float32)
elif dims == 3:
return np.ones([3, 64, 64]).astype(np.float32)
else:
return np.ones([1, 3, 64, 64]).astype(np.float32)
for dims in [1, 2, 3, 4]:
for alpha in [1.0, 2.0, 3.0]:
self.dims = dims
dics = [{"alpha": alpha}]
ops_config = [
{
"op_type": "celu",
"op_inputs": {
"X": ["input_data"],
},
"op_outputs": {"Out": ["output_data"]},
"op_attrs": dics[0],
}
]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data": TensorConfig(
data_gen=partial(generate_input1, dims, dics)
)
},
outputs=["output_data"],
)
yield program_config
def sample_predictor_configs(
self, program_config
) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
if self.dims == 1:
self.dynamic_shape.min_input_shape = {"input_data": [1]}
self.dynamic_shape.max_input_shape = {"input_data": [128]}
self.dynamic_shape.opt_input_shape = {"input_data": [64]}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {"input_data": [1, 32]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 64]}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 64]}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]}
self.dynamic_shape.max_input_shape = {
"input_data": [10, 64, 64]
}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 64, 64]}
else:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 3, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 3, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 3, 64, 64]
}
def clear_dynamic_shape():
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.max_input_shape = {}
self.dynamic_shape.opt_input_shape = {}
def generate_trt_nodes_num(attrs, dynamic_shape):
if self.dims == 1:
return 0, 3
return 1, 2
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
# for static_shape
clear_dynamic_shape()
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False
), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False
), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True
), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True
), (1e-3, 1e-3)
def test(self):
self.run_test()
if __name__ == "__main__":
unittest.main()
test/ir/inference/test_trt_convert_logsigmoid.py 已删除 100755 → 0
浏览文件 @ 6d9bbee3
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from functools import partial
from typing import Any, Dict, List
import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import TrtLayerAutoScanTest
import paddle.inference as paddle_infer
class TrtConvertLogSigmoidTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def sample_program_configs(self):
def generate_input1(dims, attrs: List[Dict[str, Any]]):
if dims == 1:
return np.ones([3]).astype(np.float32)
elif dims == 2:
return np.ones([3, 64]).astype(np.float32)
elif dims == 3:
return np.ones([3, 64, 64]).astype(np.float32)
else:
return np.ones([1, 3, 64, 64]).astype(np.float32)
for dims in [1, 2, 3, 4]:
self.dims = dims
ops_config = [
{
"op_type": "logsigmoid",
"op_inputs": {
"X": ["input_data"],
},
"op_outputs": {"Out": ["output_data"]},
"op_attrs": {},
}
]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data": TensorConfig(
data_gen=partial(generate_input1, dims, {})
)
},
outputs=["output_data"],
)
yield program_config
def sample_predictor_configs(
self, program_config
) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
if self.dims == 1:
self.dynamic_shape.min_input_shape = {"input_data": [1]}
self.dynamic_shape.max_input_shape = {"input_data": [128]}
self.dynamic_shape.opt_input_shape = {"input_data": [64]}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {"input_data": [1, 32]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 64]}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 64]}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]}
self.dynamic_shape.max_input_shape = {
"input_data": [10, 64, 64]
}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 64, 64]}
else:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 3, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 3, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 3, 64, 64]
}
def clear_dynamic_shape():
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.max_input_shape = {}
self.dynamic_shape.opt_input_shape = {}
def generate_trt_nodes_num(attrs, dynamic_shape):
if self.dims == 1:
return 0, 3
return 1, 2
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
# for static_shape
clear_dynamic_shape()
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False
), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False
), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True
), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True
), (1e-3, 1e-3)
def test(self):
self.run_test()
if __name__ == "__main__":
unittest.main()
test/ir/inference/test_trt_convert_silu.py 已删除 100755 → 0
浏览文件 @ 6d9bbee3
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from functools import partial
from typing import Any, Dict, List
import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import TrtLayerAutoScanTest
import paddle.inference as paddle_infer
class TrtConvertSiluTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def sample_program_configs(self):
def generate_input1(dims, attrs: List[Dict[str, Any]]):
if dims == 1:
return np.ones([3]).astype(np.float32)
elif dims == 2:
return np.ones([3, 64]).astype(np.float32)
elif dims == 3:
return np.ones([3, 64, 64]).astype(np.float32)
else:
return np.ones([1, 3, 64, 64]).astype(np.float32)
for dims in [1, 2, 3, 4]:
self.dims = dims
ops_config = [
{
"op_type": "silu",
"op_inputs": {
"X": ["input_data"],
},
"op_outputs": {"Out": ["output_data"]},
"op_attrs": {},
}
]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data": TensorConfig(
data_gen=partial(generate_input1, dims, {})
)
},
outputs=["output_data"],
)
yield program_config
def sample_predictor_configs(
self, program_config
) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
if self.dims == 1:
self.dynamic_shape.min_input_shape = {"input_data": [1]}
self.dynamic_shape.max_input_shape = {"input_data": [128]}
self.dynamic_shape.opt_input_shape = {"input_data": [64]}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {"input_data": [1, 32]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 64]}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 64]}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]}
self.dynamic_shape.max_input_shape = {
"input_data": [10, 64, 64]
}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 64, 64]}
else:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 3, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 3, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 3, 64, 64]
}
def clear_dynamic_shape():
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.max_input_shape = {}
self.dynamic_shape.opt_input_shape = {}
def generate_trt_nodes_num(attrs, dynamic_shape):
if self.dims == 1:
return 0, 3
return 1, 2
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
# for static_shape
clear_dynamic_shape()
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False
), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False
), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True
), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True
), (1e-3, 1e-3)
def test(self):
self.run_test()
if __name__ == "__main__":
unittest.main()
test/ir/inference/test_trt_convert_tanhshrink.py 已删除 100755 → 0
浏览文件 @ 6d9bbee3
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from functools import partial
from typing import Any, Dict, List
import numpy as np
from program_config import ProgramConfig, TensorConfig
from trt_layer_auto_scan_test import TrtLayerAutoScanTest
import paddle.inference as paddle_infer
class TrtConvertTanhshrinkTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True
def sample_program_configs(self):
def generate_input1(dims, attrs: List[Dict[str, Any]]):
if dims == 1:
return np.ones([3]).astype(np.float32)
elif dims == 2:
return np.ones([3, 64]).astype(np.float32)
elif dims == 3:
return np.ones([3, 64, 64]).astype(np.float32)
else:
return np.ones([1, 3, 64, 64]).astype(np.float32)
for dims in [1, 2, 3, 4]:
self.dims = dims
ops_config = [
{
"op_type": "tanh_shrink",
"op_inputs": {
"X": ["input_data"],
},
"op_outputs": {"Out": ["output_data"]},
"op_attrs": {},
}
]
ops = self.generate_op_config(ops_config)
program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data": TensorConfig(
data_gen=partial(generate_input1, dims, {})
)
},
outputs=["output_data"],
)
yield program_config
def sample_predictor_configs(
self, program_config
) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
if self.dims == 1:
self.dynamic_shape.min_input_shape = {"input_data": [1]}
self.dynamic_shape.max_input_shape = {"input_data": [128]}
self.dynamic_shape.opt_input_shape = {"input_data": [64]}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {"input_data": [1, 32]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 64]}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 64]}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]}
self.dynamic_shape.max_input_shape = {
"input_data": [10, 64, 64]
}
self.dynamic_shape.opt_input_shape = {"input_data": [3, 64, 64]}
else:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 3, 32, 32]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 3, 64, 64]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [1, 3, 64, 64]
}
def clear_dynamic_shape():
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.max_input_shape = {}
self.dynamic_shape.opt_input_shape = {}
def generate_trt_nodes_num(attrs, dynamic_shape):
if self.dims == 1:
return 0, 3
return 1, 2
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
# for static_shape
clear_dynamic_shape()
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False
), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False
), (1e-3, 1e-3)
# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True
), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True
), (1e-3, 1e-3)
def test(self):
self.run_test()
if __name__ == "__main__":
unittest.main()
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